{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.124086</td>\n",
       "      <td>0.123077</td>\n",
       "      <td>0.124692</td>\n",
       "      <td>0.123098</td>\n",
       "      <td>0.121993</td>\n",
       "      <td>0.120885</td>\n",
       "      <td>0.120650</td>\n",
       "      <td>0.122520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125157</td>\n",
       "      <td>0.121047</td>\n",
       "      <td>0.119458</td>\n",
       "      <td>0.121360</td>\n",
       "      <td>0.123017</td>\n",
       "      <td>0.120512</td>\n",
       "      <td>0.123048</td>\n",
       "      <td>0.122855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.125133</td>\n",
       "      <td>0.125864</td>\n",
       "      <td>0.122109</td>\n",
       "      <td>0.126549</td>\n",
       "      <td>0.124533</td>\n",
       "      <td>0.121847</td>\n",
       "      <td>0.120396</td>\n",
       "      <td>0.123333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.125903</td>\n",
       "      <td>0.122256</td>\n",
       "      <td>0.125582</td>\n",
       "      <td>0.123162</td>\n",
       "      <td>0.122565</td>\n",
       "      <td>0.122018</td>\n",
       "      <td>0.124485</td>\n",
       "      <td>0.125027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.123719</td>\n",
       "      <td>0.128365</td>\n",
       "      <td>0.121615</td>\n",
       "      <td>0.123504</td>\n",
       "      <td>0.119830</td>\n",
       "      <td>0.126062</td>\n",
       "      <td>0.123403</td>\n",
       "      <td>0.122291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.124086  0.123077  0.124692  0.123098  0.121993  0.120885  0.120650   \n",
       "1  0.125157  0.121047  0.119458  0.121360  0.123017  0.120512  0.123048   \n",
       "2  0.125133  0.125864  0.122109  0.126549  0.124533  0.121847  0.120396   \n",
       "3  0.125903  0.122256  0.125582  0.123162  0.122565  0.122018  0.124485   \n",
       "4  0.123719  0.128365  0.121615  0.123504  0.119830  0.126062  0.123403   \n",
       "\n",
       "          7  \n",
       "0  0.122520  \n",
       "1  0.122855  \n",
       "2  0.123333  \n",
       "3  0.125027  \n",
       "4  0.122291  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/data_rms_bearing_1.csv', header=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.152867</td>\n",
       "      <td>0.153102</td>\n",
       "      <td>0.151212</td>\n",
       "      <td>0.150680</td>\n",
       "      <td>0.153969</td>\n",
       "      <td>0.150338</td>\n",
       "      <td>0.150380</td>\n",
       "      <td>0.150312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.012501</td>\n",
       "      <td>0.012814</td>\n",
       "      <td>0.013307</td>\n",
       "      <td>0.012899</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.013063</td>\n",
       "      <td>0.012918</td>\n",
       "      <td>0.012780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.119746</td>\n",
       "      <td>0.117820</td>\n",
       "      <td>0.116974</td>\n",
       "      <td>0.115786</td>\n",
       "      <td>0.119502</td>\n",
       "      <td>0.116332</td>\n",
       "      <td>0.116370</td>\n",
       "      <td>0.117361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.149213</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.147060</td>\n",
       "      <td>0.146744</td>\n",
       "      <td>0.150438</td>\n",
       "      <td>0.146495</td>\n",
       "      <td>0.146565</td>\n",
       "      <td>0.146482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.151007</td>\n",
       "      <td>0.151351</td>\n",
       "      <td>0.149639</td>\n",
       "      <td>0.148874</td>\n",
       "      <td>0.152409</td>\n",
       "      <td>0.148438</td>\n",
       "      <td>0.148448</td>\n",
       "      <td>0.148342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.156103</td>\n",
       "      <td>0.156384</td>\n",
       "      <td>0.154420</td>\n",
       "      <td>0.154026</td>\n",
       "      <td>0.158056</td>\n",
       "      <td>0.153872</td>\n",
       "      <td>0.153553</td>\n",
       "      <td>0.153606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.326161</td>\n",
       "      <td>0.325306</td>\n",
       "      <td>0.377078</td>\n",
       "      <td>0.349152</td>\n",
       "      <td>0.322789</td>\n",
       "      <td>0.347876</td>\n",
       "      <td>0.358188</td>\n",
       "      <td>0.320975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3            4  \\\n",
       "count  2156.000000  2156.000000  2156.000000  2156.000000  2156.000000   \n",
       "mean      0.152867     0.153102     0.151212     0.150680     0.153969   \n",
       "std       0.012501     0.012814     0.013307     0.012899     0.013333   \n",
       "min       0.119746     0.117820     0.116974     0.115786     0.119502   \n",
       "25%       0.149213     0.149268     0.147060     0.146744     0.150438   \n",
       "50%       0.151007     0.151351     0.149639     0.148874     0.152409   \n",
       "75%       0.156103     0.156384     0.154420     0.154026     0.158056   \n",
       "max       0.326161     0.325306     0.377078     0.349152     0.322789   \n",
       "\n",
       "                 5            6            7  \n",
       "count  2156.000000  2156.000000  2156.000000  \n",
       "mean      0.150338     0.150380     0.150312  \n",
       "std       0.013063     0.012918     0.012780  \n",
       "min       0.116332     0.116370     0.117361  \n",
       "25%       0.146495     0.146565     0.146482  \n",
       "50%       0.148438     0.148448     0.148342  \n",
       "75%       0.153872     0.153553     0.153606  \n",
       "max       0.347876     0.358188     0.320975  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2156 entries, 0 to 2155\n",
      "Data columns (total 8 columns):\n",
      "0    2156 non-null float64\n",
      "1    2156 non-null float64\n",
      "2    2156 non-null float64\n",
      "3    2156 non-null float64\n",
      "4    2156 non-null float64\n",
      "5    2156 non-null float64\n",
      "6    2156 non-null float64\n",
      "7    2156 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 134.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1a97f6cf8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gives us a quick visual inspection of the data\n",
    "msno.matrix(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# waktu\n",
    "minutes = []\n",
    "time = 0\n",
    "for i in range(2156):\n",
    "    minutes.append(time)\n",
    "    if i < 42:\n",
    "        time += 5\n",
    "    else:\n",
    "        time += 10\n",
    "        \n",
    "type(minutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.124086</td>\n",
       "      <td>0.123077</td>\n",
       "      <td>0.124692</td>\n",
       "      <td>0.123098</td>\n",
       "      <td>0.121993</td>\n",
       "      <td>0.120885</td>\n",
       "      <td>0.120650</td>\n",
       "      <td>0.122520</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125157</td>\n",
       "      <td>0.121047</td>\n",
       "      <td>0.119458</td>\n",
       "      <td>0.121360</td>\n",
       "      <td>0.123017</td>\n",
       "      <td>0.120512</td>\n",
       "      <td>0.123048</td>\n",
       "      <td>0.122855</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.125133</td>\n",
       "      <td>0.125864</td>\n",
       "      <td>0.122109</td>\n",
       "      <td>0.126549</td>\n",
       "      <td>0.124533</td>\n",
       "      <td>0.121847</td>\n",
       "      <td>0.120396</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.125903</td>\n",
       "      <td>0.122256</td>\n",
       "      <td>0.125582</td>\n",
       "      <td>0.123162</td>\n",
       "      <td>0.122565</td>\n",
       "      <td>0.122018</td>\n",
       "      <td>0.124485</td>\n",
       "      <td>0.125027</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.123719</td>\n",
       "      <td>0.128365</td>\n",
       "      <td>0.121615</td>\n",
       "      <td>0.123504</td>\n",
       "      <td>0.119830</td>\n",
       "      <td>0.126062</td>\n",
       "      <td>0.123403</td>\n",
       "      <td>0.122291</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.128433</td>\n",
       "      <td>0.126848</td>\n",
       "      <td>0.123987</td>\n",
       "      <td>0.121869</td>\n",
       "      <td>0.122248</td>\n",
       "      <td>0.127132</td>\n",
       "      <td>0.124007</td>\n",
       "      <td>0.123718</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.122857</td>\n",
       "      <td>0.120683</td>\n",
       "      <td>0.119108</td>\n",
       "      <td>0.115786</td>\n",
       "      <td>0.119985</td>\n",
       "      <td>0.118659</td>\n",
       "      <td>0.120145</td>\n",
       "      <td>0.117361</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.125447</td>\n",
       "      <td>0.125975</td>\n",
       "      <td>0.122006</td>\n",
       "      <td>0.125328</td>\n",
       "      <td>0.125551</td>\n",
       "      <td>0.124281</td>\n",
       "      <td>0.119824</td>\n",
       "      <td>0.122376</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.124211</td>\n",
       "      <td>0.122625</td>\n",
       "      <td>0.124562</td>\n",
       "      <td>0.121111</td>\n",
       "      <td>0.119881</td>\n",
       "      <td>0.116332</td>\n",
       "      <td>0.117253</td>\n",
       "      <td>0.118600</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.122786</td>\n",
       "      <td>0.123712</td>\n",
       "      <td>0.122882</td>\n",
       "      <td>0.120993</td>\n",
       "      <td>0.121802</td>\n",
       "      <td>0.119418</td>\n",
       "      <td>0.122520</td>\n",
       "      <td>0.121696</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.126290</td>\n",
       "      <td>0.127203</td>\n",
       "      <td>0.124348</td>\n",
       "      <td>0.124735</td>\n",
       "      <td>0.123639</td>\n",
       "      <td>0.126451</td>\n",
       "      <td>0.125090</td>\n",
       "      <td>0.125531</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.126076</td>\n",
       "      <td>0.122833</td>\n",
       "      <td>0.122171</td>\n",
       "      <td>0.120602</td>\n",
       "      <td>0.122401</td>\n",
       "      <td>0.121324</td>\n",
       "      <td>0.122490</td>\n",
       "      <td>0.119325</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.124367</td>\n",
       "      <td>0.126282</td>\n",
       "      <td>0.120932</td>\n",
       "      <td>0.124046</td>\n",
       "      <td>0.122122</td>\n",
       "      <td>0.121129</td>\n",
       "      <td>0.119563</td>\n",
       "      <td>0.122260</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.126360</td>\n",
       "      <td>0.123026</td>\n",
       "      <td>0.122690</td>\n",
       "      <td>0.125110</td>\n",
       "      <td>0.121277</td>\n",
       "      <td>0.121893</td>\n",
       "      <td>0.120165</td>\n",
       "      <td>0.125441</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.124660</td>\n",
       "      <td>0.122741</td>\n",
       "      <td>0.123929</td>\n",
       "      <td>0.121601</td>\n",
       "      <td>0.121558</td>\n",
       "      <td>0.119066</td>\n",
       "      <td>0.124142</td>\n",
       "      <td>0.124673</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.123163</td>\n",
       "      <td>0.127033</td>\n",
       "      <td>0.124319</td>\n",
       "      <td>0.122448</td>\n",
       "      <td>0.122152</td>\n",
       "      <td>0.121918</td>\n",
       "      <td>0.126459</td>\n",
       "      <td>0.124113</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.124697</td>\n",
       "      <td>0.128094</td>\n",
       "      <td>0.125090</td>\n",
       "      <td>0.123108</td>\n",
       "      <td>0.119865</td>\n",
       "      <td>0.122793</td>\n",
       "      <td>0.122986</td>\n",
       "      <td>0.121857</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.125176</td>\n",
       "      <td>0.127415</td>\n",
       "      <td>0.122372</td>\n",
       "      <td>0.124390</td>\n",
       "      <td>0.121952</td>\n",
       "      <td>0.126539</td>\n",
       "      <td>0.123912</td>\n",
       "      <td>0.122854</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.126223</td>\n",
       "      <td>0.125396</td>\n",
       "      <td>0.123454</td>\n",
       "      <td>0.120098</td>\n",
       "      <td>0.125865</td>\n",
       "      <td>0.125992</td>\n",
       "      <td>0.123048</td>\n",
       "      <td>0.120458</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.121850</td>\n",
       "      <td>0.123050</td>\n",
       "      <td>0.116974</td>\n",
       "      <td>0.121585</td>\n",
       "      <td>0.120202</td>\n",
       "      <td>0.117283</td>\n",
       "      <td>0.116370</td>\n",
       "      <td>0.117717</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.126209</td>\n",
       "      <td>0.125166</td>\n",
       "      <td>0.122181</td>\n",
       "      <td>0.125603</td>\n",
       "      <td>0.120910</td>\n",
       "      <td>0.123817</td>\n",
       "      <td>0.121550</td>\n",
       "      <td>0.124242</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.126093</td>\n",
       "      <td>0.126154</td>\n",
       "      <td>0.125882</td>\n",
       "      <td>0.124261</td>\n",
       "      <td>0.123966</td>\n",
       "      <td>0.119609</td>\n",
       "      <td>0.122744</td>\n",
       "      <td>0.123579</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.126973</td>\n",
       "      <td>0.122155</td>\n",
       "      <td>0.124186</td>\n",
       "      <td>0.124233</td>\n",
       "      <td>0.122203</td>\n",
       "      <td>0.120363</td>\n",
       "      <td>0.121963</td>\n",
       "      <td>0.126046</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.132048</td>\n",
       "      <td>0.131279</td>\n",
       "      <td>0.129910</td>\n",
       "      <td>0.127126</td>\n",
       "      <td>0.128555</td>\n",
       "      <td>0.124212</td>\n",
       "      <td>0.130912</td>\n",
       "      <td>0.127725</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.146345</td>\n",
       "      <td>0.145882</td>\n",
       "      <td>0.143730</td>\n",
       "      <td>0.143687</td>\n",
       "      <td>0.145256</td>\n",
       "      <td>0.144592</td>\n",
       "      <td>0.141048</td>\n",
       "      <td>0.143411</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.123260</td>\n",
       "      <td>0.122398</td>\n",
       "      <td>0.123374</td>\n",
       "      <td>0.126336</td>\n",
       "      <td>0.122896</td>\n",
       "      <td>0.123937</td>\n",
       "      <td>0.120356</td>\n",
       "      <td>0.124410</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.126420</td>\n",
       "      <td>0.125176</td>\n",
       "      <td>0.121051</td>\n",
       "      <td>0.122837</td>\n",
       "      <td>0.124463</td>\n",
       "      <td>0.124119</td>\n",
       "      <td>0.122626</td>\n",
       "      <td>0.123736</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.125520</td>\n",
       "      <td>0.124725</td>\n",
       "      <td>0.123041</td>\n",
       "      <td>0.125523</td>\n",
       "      <td>0.122607</td>\n",
       "      <td>0.124158</td>\n",
       "      <td>0.121237</td>\n",
       "      <td>0.127856</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.124799</td>\n",
       "      <td>0.122674</td>\n",
       "      <td>0.126416</td>\n",
       "      <td>0.122198</td>\n",
       "      <td>0.122763</td>\n",
       "      <td>0.122443</td>\n",
       "      <td>0.127137</td>\n",
       "      <td>0.122820</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.125371</td>\n",
       "      <td>0.126955</td>\n",
       "      <td>0.122839</td>\n",
       "      <td>0.123502</td>\n",
       "      <td>0.121232</td>\n",
       "      <td>0.126231</td>\n",
       "      <td>0.124148</td>\n",
       "      <td>0.124270</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.127117</td>\n",
       "      <td>0.125399</td>\n",
       "      <td>0.122240</td>\n",
       "      <td>0.123795</td>\n",
       "      <td>0.126147</td>\n",
       "      <td>0.122344</td>\n",
       "      <td>0.122525</td>\n",
       "      <td>0.122851</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.125403</td>\n",
       "      <td>0.127024</td>\n",
       "      <td>0.121282</td>\n",
       "      <td>0.125943</td>\n",
       "      <td>0.122842</td>\n",
       "      <td>0.123198</td>\n",
       "      <td>0.121221</td>\n",
       "      <td>0.124434</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.124794</td>\n",
       "      <td>0.124103</td>\n",
       "      <td>0.125022</td>\n",
       "      <td>0.124230</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.121875</td>\n",
       "      <td>0.126712</td>\n",
       "      <td>0.124738</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.125229</td>\n",
       "      <td>0.127598</td>\n",
       "      <td>0.124949</td>\n",
       "      <td>0.125388</td>\n",
       "      <td>0.121931</td>\n",
       "      <td>0.122032</td>\n",
       "      <td>0.125445</td>\n",
       "      <td>0.123148</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.127932</td>\n",
       "      <td>0.127270</td>\n",
       "      <td>0.122341</td>\n",
       "      <td>0.122133</td>\n",
       "      <td>0.124038</td>\n",
       "      <td>0.126044</td>\n",
       "      <td>0.123096</td>\n",
       "      <td>0.122329</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.124983</td>\n",
       "      <td>0.125584</td>\n",
       "      <td>0.121452</td>\n",
       "      <td>0.121599</td>\n",
       "      <td>0.123217</td>\n",
       "      <td>0.121340</td>\n",
       "      <td>0.124749</td>\n",
       "      <td>0.121667</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.123031</td>\n",
       "      <td>0.124721</td>\n",
       "      <td>0.120395</td>\n",
       "      <td>0.121399</td>\n",
       "      <td>0.119502</td>\n",
       "      <td>0.121290</td>\n",
       "      <td>0.119398</td>\n",
       "      <td>0.121508</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.123587</td>\n",
       "      <td>0.128161</td>\n",
       "      <td>0.123168</td>\n",
       "      <td>0.121539</td>\n",
       "      <td>0.121069</td>\n",
       "      <td>0.124560</td>\n",
       "      <td>0.121234</td>\n",
       "      <td>0.122034</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.128272</td>\n",
       "      <td>0.124081</td>\n",
       "      <td>0.122822</td>\n",
       "      <td>0.121378</td>\n",
       "      <td>0.123787</td>\n",
       "      <td>0.123589</td>\n",
       "      <td>0.121799</td>\n",
       "      <td>0.120671</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.124683</td>\n",
       "      <td>0.124472</td>\n",
       "      <td>0.123815</td>\n",
       "      <td>0.125107</td>\n",
       "      <td>0.121239</td>\n",
       "      <td>0.121668</td>\n",
       "      <td>0.119676</td>\n",
       "      <td>0.124425</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.123002</td>\n",
       "      <td>0.125893</td>\n",
       "      <td>0.122623</td>\n",
       "      <td>0.121088</td>\n",
       "      <td>0.124145</td>\n",
       "      <td>0.120683</td>\n",
       "      <td>0.125593</td>\n",
       "      <td>0.125018</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.119746</td>\n",
       "      <td>0.117820</td>\n",
       "      <td>0.117517</td>\n",
       "      <td>0.121608</td>\n",
       "      <td>0.121021</td>\n",
       "      <td>0.121050</td>\n",
       "      <td>0.119722</td>\n",
       "      <td>0.123836</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.126672</td>\n",
       "      <td>0.120526</td>\n",
       "      <td>0.123930</td>\n",
       "      <td>0.123157</td>\n",
       "      <td>0.122298</td>\n",
       "      <td>0.119372</td>\n",
       "      <td>0.121587</td>\n",
       "      <td>0.123053</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.124900</td>\n",
       "      <td>0.122537</td>\n",
       "      <td>0.123977</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>0.122506</td>\n",
       "      <td>0.122848</td>\n",
       "      <td>0.120114</td>\n",
       "      <td>0.126746</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.126478</td>\n",
       "      <td>0.124965</td>\n",
       "      <td>0.125156</td>\n",
       "      <td>0.123455</td>\n",
       "      <td>0.123180</td>\n",
       "      <td>0.122391</td>\n",
       "      <td>0.124811</td>\n",
       "      <td>0.123030</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.123394</td>\n",
       "      <td>0.130805</td>\n",
       "      <td>0.124242</td>\n",
       "      <td>0.123129</td>\n",
       "      <td>0.121852</td>\n",
       "      <td>0.123690</td>\n",
       "      <td>0.124191</td>\n",
       "      <td>0.122620</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.127167</td>\n",
       "      <td>0.128919</td>\n",
       "      <td>0.122740</td>\n",
       "      <td>0.120199</td>\n",
       "      <td>0.123971</td>\n",
       "      <td>0.123735</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.120256</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.127980</td>\n",
       "      <td>0.125772</td>\n",
       "      <td>0.122941</td>\n",
       "      <td>0.121641</td>\n",
       "      <td>0.124496</td>\n",
       "      <td>0.123526</td>\n",
       "      <td>0.121582</td>\n",
       "      <td>0.121453</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.126808</td>\n",
       "      <td>0.124795</td>\n",
       "      <td>0.121504</td>\n",
       "      <td>0.124902</td>\n",
       "      <td>0.124415</td>\n",
       "      <td>0.123278</td>\n",
       "      <td>0.124648</td>\n",
       "      <td>0.122651</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.126491</td>\n",
       "      <td>0.120897</td>\n",
       "      <td>0.121875</td>\n",
       "      <td>0.124424</td>\n",
       "      <td>0.122176</td>\n",
       "      <td>0.123379</td>\n",
       "      <td>0.122804</td>\n",
       "      <td>0.126392</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0   0.124086  0.123077  0.124692  0.123098  0.121993  0.120885  0.120650   \n",
       "1   0.125157  0.121047  0.119458  0.121360  0.123017  0.120512  0.123048   \n",
       "2   0.125133  0.125864  0.122109  0.126549  0.124533  0.121847  0.120396   \n",
       "3   0.125903  0.122256  0.125582  0.123162  0.122565  0.122018  0.124485   \n",
       "4   0.123719  0.128365  0.121615  0.123504  0.119830  0.126062  0.123403   \n",
       "5   0.128433  0.126848  0.123987  0.121869  0.122248  0.127132  0.124007   \n",
       "6   0.122857  0.120683  0.119108  0.115786  0.119985  0.118659  0.120145   \n",
       "7   0.125447  0.125975  0.122006  0.125328  0.125551  0.124281  0.119824   \n",
       "8   0.124211  0.122625  0.124562  0.121111  0.119881  0.116332  0.117253   \n",
       "9   0.122786  0.123712  0.122882  0.120993  0.121802  0.119418  0.122520   \n",
       "10  0.126290  0.127203  0.124348  0.124735  0.123639  0.126451  0.125090   \n",
       "11  0.126076  0.122833  0.122171  0.120602  0.122401  0.121324  0.122490   \n",
       "12  0.124367  0.126282  0.120932  0.124046  0.122122  0.121129  0.119563   \n",
       "13  0.126360  0.123026  0.122690  0.125110  0.121277  0.121893  0.120165   \n",
       "14  0.124660  0.122741  0.123929  0.121601  0.121558  0.119066  0.124142   \n",
       "15  0.123163  0.127033  0.124319  0.122448  0.122152  0.121918  0.126459   \n",
       "16  0.124697  0.128094  0.125090  0.123108  0.119865  0.122793  0.122986   \n",
       "17  0.125176  0.127415  0.122372  0.124390  0.121952  0.126539  0.123912   \n",
       "18  0.126223  0.125396  0.123454  0.120098  0.125865  0.125992  0.123048   \n",
       "19  0.121850  0.123050  0.116974  0.121585  0.120202  0.117283  0.116370   \n",
       "20  0.126209  0.125166  0.122181  0.125603  0.120910  0.123817  0.121550   \n",
       "21  0.126093  0.126154  0.125882  0.124261  0.123966  0.119609  0.122744   \n",
       "22  0.126973  0.122155  0.124186  0.124233  0.122203  0.120363  0.121963   \n",
       "23  0.132048  0.131279  0.129910  0.127126  0.128555  0.124212  0.130912   \n",
       "24  0.146345  0.145882  0.143730  0.143687  0.145256  0.144592  0.141048   \n",
       "25  0.123260  0.122398  0.123374  0.126336  0.122896  0.123937  0.120356   \n",
       "26  0.126420  0.125176  0.121051  0.122837  0.124463  0.124119  0.122626   \n",
       "27  0.125520  0.124725  0.123041  0.125523  0.122607  0.124158  0.121237   \n",
       "28  0.124799  0.122674  0.126416  0.122198  0.122763  0.122443  0.127137   \n",
       "29  0.125371  0.126955  0.122839  0.123502  0.121232  0.126231  0.124148   \n",
       "30  0.127117  0.125399  0.122240  0.123795  0.126147  0.122344  0.122525   \n",
       "31  0.125403  0.127024  0.121282  0.125943  0.122842  0.123198  0.121221   \n",
       "32  0.124794  0.124103  0.125022  0.124230  0.122498  0.121875  0.126712   \n",
       "33  0.125229  0.127598  0.124949  0.125388  0.121931  0.122032  0.125445   \n",
       "34  0.127932  0.127270  0.122341  0.122133  0.124038  0.126044  0.123096   \n",
       "35  0.124983  0.125584  0.121452  0.121599  0.123217  0.121340  0.124749   \n",
       "36  0.123031  0.124721  0.120395  0.121399  0.119502  0.121290  0.119398   \n",
       "37  0.123587  0.128161  0.123168  0.121539  0.121069  0.124560  0.121234   \n",
       "38  0.128272  0.124081  0.122822  0.121378  0.123787  0.123589  0.121799   \n",
       "39  0.124683  0.124472  0.123815  0.125107  0.121239  0.121668  0.119676   \n",
       "40  0.123002  0.125893  0.122623  0.121088  0.124145  0.120683  0.125593   \n",
       "41  0.119746  0.117820  0.117517  0.121608  0.121021  0.121050  0.119722   \n",
       "42  0.126672  0.120526  0.123930  0.123157  0.122298  0.119372  0.121587   \n",
       "43  0.124900  0.122537  0.123977  0.125661  0.122506  0.122848  0.120114   \n",
       "44  0.126478  0.124965  0.125156  0.123455  0.123180  0.122391  0.124811   \n",
       "45  0.123394  0.130805  0.124242  0.123129  0.121852  0.123690  0.124191   \n",
       "46  0.127167  0.128919  0.122740  0.120199  0.123971  0.123735  0.121759   \n",
       "47  0.127980  0.125772  0.122941  0.121641  0.124496  0.123526  0.121582   \n",
       "48  0.126808  0.124795  0.121504  0.124902  0.124415  0.123278  0.124648   \n",
       "49  0.126491  0.120897  0.121875  0.124424  0.122176  0.123379  0.122804   \n",
       "\n",
       "           7  times  \n",
       "0   0.122520      0  \n",
       "1   0.122855      5  \n",
       "2   0.123333     10  \n",
       "3   0.125027     15  \n",
       "4   0.122291     20  \n",
       "5   0.123718     25  \n",
       "6   0.117361     30  \n",
       "7   0.122376     35  \n",
       "8   0.118600     40  \n",
       "9   0.121696     45  \n",
       "10  0.125531     50  \n",
       "11  0.119325     55  \n",
       "12  0.122260     60  \n",
       "13  0.125441     65  \n",
       "14  0.124673     70  \n",
       "15  0.124113     75  \n",
       "16  0.121857     80  \n",
       "17  0.122854     85  \n",
       "18  0.120458     90  \n",
       "19  0.117717     95  \n",
       "20  0.124242    100  \n",
       "21  0.123579    105  \n",
       "22  0.126046    110  \n",
       "23  0.127725    115  \n",
       "24  0.143411    120  \n",
       "25  0.124410    125  \n",
       "26  0.123736    130  \n",
       "27  0.127856    135  \n",
       "28  0.122820    140  \n",
       "29  0.124270    145  \n",
       "30  0.122851    150  \n",
       "31  0.124434    155  \n",
       "32  0.124738    160  \n",
       "33  0.123148    165  \n",
       "34  0.122329    170  \n",
       "35  0.121667    175  \n",
       "36  0.121508    180  \n",
       "37  0.122034    185  \n",
       "38  0.120671    190  \n",
       "39  0.124425    195  \n",
       "40  0.125018    200  \n",
       "41  0.123836    205  \n",
       "42  0.123053    210  \n",
       "43  0.126746    220  \n",
       "44  0.123030    230  \n",
       "45  0.122620    240  \n",
       "46  0.120256    250  \n",
       "47  0.121453    260  \n",
       "48  0.122651    270  \n",
       "49  0.126392    280  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# masukkan ke dalam pandas data\n",
    "data['times'] = pd.Series(minutes).values\n",
    "data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,0,'Time(min)')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1584 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(8, sharex=True,  sharey=True, figsize=(12, 22))\n",
    "colors = cm.rainbow(np.linspace(0, 1, 8))\n",
    "\n",
    "for i, (ax, color) in enumerate(zip(axs, colors)):\n",
    "    mask = data['times'] == i\n",
    "    ax.plot(data['times'], data[i], c=color)\n",
    "    ax.set_title(\"{0}th signal\".format(i), fontsize=16)\n",
    "    plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
    "ax.set(xlabel='Time(min)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.152867</td>\n",
       "      <td>0.153102</td>\n",
       "      <td>0.151212</td>\n",
       "      <td>0.150680</td>\n",
       "      <td>0.153969</td>\n",
       "      <td>0.150338</td>\n",
       "      <td>0.150380</td>\n",
       "      <td>0.150312</td>\n",
       "      <td>10567.094156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.012501</td>\n",
       "      <td>0.012814</td>\n",
       "      <td>0.013307</td>\n",
       "      <td>0.012899</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.013063</td>\n",
       "      <td>0.012918</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>6221.721232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.119746</td>\n",
       "      <td>0.117820</td>\n",
       "      <td>0.116974</td>\n",
       "      <td>0.115786</td>\n",
       "      <td>0.119502</td>\n",
       "      <td>0.116332</td>\n",
       "      <td>0.116370</td>\n",
       "      <td>0.117361</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.149213</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.147060</td>\n",
       "      <td>0.146744</td>\n",
       "      <td>0.150438</td>\n",
       "      <td>0.146495</td>\n",
       "      <td>0.146565</td>\n",
       "      <td>0.146482</td>\n",
       "      <td>5177.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.151007</td>\n",
       "      <td>0.151351</td>\n",
       "      <td>0.149639</td>\n",
       "      <td>0.148874</td>\n",
       "      <td>0.152409</td>\n",
       "      <td>0.148438</td>\n",
       "      <td>0.148448</td>\n",
       "      <td>0.148342</td>\n",
       "      <td>10565.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.156103</td>\n",
       "      <td>0.156384</td>\n",
       "      <td>0.154420</td>\n",
       "      <td>0.154026</td>\n",
       "      <td>0.158056</td>\n",
       "      <td>0.153872</td>\n",
       "      <td>0.153553</td>\n",
       "      <td>0.153606</td>\n",
       "      <td>15952.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.326161</td>\n",
       "      <td>0.325306</td>\n",
       "      <td>0.377078</td>\n",
       "      <td>0.349152</td>\n",
       "      <td>0.322789</td>\n",
       "      <td>0.347876</td>\n",
       "      <td>0.358188</td>\n",
       "      <td>0.320975</td>\n",
       "      <td>21340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3            4  \\\n",
       "count  2156.000000  2156.000000  2156.000000  2156.000000  2156.000000   \n",
       "mean      0.152867     0.153102     0.151212     0.150680     0.153969   \n",
       "std       0.012501     0.012814     0.013307     0.012899     0.013333   \n",
       "min       0.119746     0.117820     0.116974     0.115786     0.119502   \n",
       "25%       0.149213     0.149268     0.147060     0.146744     0.150438   \n",
       "50%       0.151007     0.151351     0.149639     0.148874     0.152409   \n",
       "75%       0.156103     0.156384     0.154420     0.154026     0.158056   \n",
       "max       0.326161     0.325306     0.377078     0.349152     0.322789   \n",
       "\n",
       "                 5            6            7         times  \n",
       "count  2156.000000  2156.000000  2156.000000   2156.000000  \n",
       "mean      0.150338     0.150380     0.150312  10567.094156  \n",
       "std       0.013063     0.012918     0.012780   6221.721232  \n",
       "min       0.116332     0.116370     0.117361      0.000000  \n",
       "25%       0.146495     0.146565     0.146482   5177.500000  \n",
       "50%       0.148438     0.148448     0.148342  10565.000000  \n",
       "75%       0.153872     0.153553     0.153606  15952.500000  \n",
       "max       0.347876     0.358188     0.320975  21340.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tentukan range\n",
    "- state 1 : <= 0.14\n",
    "- state 2 : 0.14 - 0.18\n",
    "- state 3 : >= 0.18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proses pada data channel 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ch0 = []\n",
    "for index, row in data.iterrows():\n",
    "    if row[0] <= 0.16:\n",
    "        data_ch0.append(0)\n",
    "    elif row[0] > 0.16 and row[0] < 0.17:\n",
    "        data_ch0.append(1)\n",
    "    elif row[0] >= 0.17:\n",
    "        data_ch0.append(2)\n",
    "\n",
    "data_ch0.count(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = [\"Green\", \"Orange\", \"Red\"]\n",
    "n_states = len(states)\n",
    "\n",
    "observations = [0, 1, 2]\n",
    "n_observations = len(observations)\n",
    "\n",
    "# initial probability\n",
    "phi = np.array([0.6, 0.2, 0.2])\n",
    "\n",
    "# transition probability\n",
    "A = np.array([[0.4, 0.3, 0.3],\n",
    "              [0.3, 0.5, 0.3],\n",
    "              [0.1, 0.3, 0.6]])\n",
    "\n",
    "# observation probability\n",
    "B = np.array([[0.2, 0.3, 0.5],\n",
    "              [0.5, 0.4, 0.1],\n",
    "              [0.5, 0.3, 0.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hmmlearn import hmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize hmm\n",
    "m_hmmlearn = hmm.MultinomialHMM(n_components=n_states, \n",
    "                                verbose=True)\n",
    "m_hmmlearn.startprob = phi\n",
    "m_hmmlearn.transmat = A\n",
    "m_hmmlearn.emissionprob = B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 2, 2, 2]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observation sequence\n",
    "obs_seq = np.array([data_ch0])\n",
    "obs_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1       -2471.5856             +nan\n",
      "         2       -1277.5247       +1194.0609\n",
      "         3       -1269.3009          +8.2237\n",
      "         4       -1256.3791         +12.9219\n",
      "         5       -1242.9858         +13.3933\n",
      "         6       -1231.2495         +11.7363\n",
      "         7       -1218.5028         +12.7467\n",
      "         8       -1198.4799         +20.0229\n",
      "         9       -1143.8221         +54.6578\n",
      "        10        -750.6504        +393.1717\n"
     ]
    }
   ],
   "source": [
    "# learning\n",
    "m_hmmlearn = m_hmmlearn.fit(obs_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_prob, best_path = m_hmmlearn.decode(obs_seq.T, algorithm='viterbi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-780.268807779286"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8056586270871985"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(np.array(data_ch0), best_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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